{"id":"https://openalex.org/W4308235676","doi":"https://doi.org/10.1109/icip46576.2022.9897418","title":"Background-Tolerant Object Classification With Embedded Segmentation Mask For Infrared And Color Imagery","display_name":"Background-Tolerant Object Classification With Embedded Segmentation Mask For Infrared And Color Imagery","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308235676","doi":"https://doi.org/10.1109/icip46576.2022.9897418"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897418","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051244661","display_name":"Maliha Arif","orcid":"https://orcid.org/0000-0002-4457-7210"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]},{"id":"https://openalex.org/I4210105317","display_name":"Vision Cooperative Research Centre","ror":"https://ror.org/01e5x8t10","country_code":"AU","type":"nonprofit","lineage":["https://openalex.org/I4210105317"]}],"countries":["AU","US"],"is_corresponding":false,"raw_author_name":"Maliha Arif","raw_affiliation_strings":["Center for Research in Computer Vision (CRCV)","University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Research in Computer Vision (CRCV)","institution_ids":["https://openalex.org/I4210105317"]},{"raw_affiliation_string":"University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007762854","display_name":"Calvin Yong","orcid":null},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Calvin Yong","raw_affiliation_strings":["University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055793435","display_name":"Abhijit Mahalanobis","orcid":"https://orcid.org/0000-0002-2782-8655"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]},{"id":"https://openalex.org/I4210105317","display_name":"Vision Cooperative Research Centre","ror":"https://ror.org/01e5x8t10","country_code":"AU","type":"nonprofit","lineage":["https://openalex.org/I4210105317"]}],"countries":["AU","US"],"is_corresponding":false,"raw_author_name":"Abhijit Mahalanobis","raw_affiliation_strings":["Center for Research in Computer Vision (CRCV)","University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Research in Computer Vision (CRCV)","institution_ids":["https://openalex.org/I4210105317"]},{"raw_affiliation_string":"University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109495751","display_name":"Nazanin Rahnavard","orcid":null},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nazanin Rahnavard","raw_affiliation_strings":["University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10577128,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1546","last_page":"1550"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8245232105255127},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8117762804031372},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6542128324508667},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6255035400390625},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.582936704158783},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5692599415779114},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5514854788780212},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5354241728782654},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5322403907775879},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.487030565738678},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4438626766204834},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4350743889808655},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4176940321922302},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41387733817100525},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23226967453956604},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09884774684906006}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8245232105255127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8117762804031372},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6542128324508667},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6255035400390625},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.582936704158783},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5692599415779114},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5514854788780212},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5354241728782654},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5322403907775879},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.487030565738678},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4438626766204834},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4350743889808655},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4176940321922302},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41387733817100525},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23226967453956604},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09884774684906006},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897418","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2037227137","https://openalex.org/W2962858109","https://openalex.org/W2963163009","https://openalex.org/W2963446712","https://openalex.org/W3037248214","https://openalex.org/W3128637142","https://openalex.org/W3193999509","https://openalex.org/W4308831279","https://openalex.org/W6637373629","https://openalex.org/W6740164494","https://openalex.org/W6756444276","https://openalex.org/W6779631978","https://openalex.org/W6779977225"],"related_works":["https://openalex.org/W2102148524","https://openalex.org/W2314720829","https://openalex.org/W2486460843","https://openalex.org/W2168109476","https://openalex.org/W4385074335","https://openalex.org/W1968121071","https://openalex.org/W2626189183","https://openalex.org/W2020254986","https://openalex.org/W4387272257","https://openalex.org/W4200301313"],"abstract_inverted_index":{"Even":[0],"though":[1],"convolutional":[2],"neural":[3],"networks":[4,40,59],"(CNNs)":[5],"can":[6],"classify":[7,190],"objects":[8,114,151,192],"in":[9,65,87,115,147,153,193,205],"images":[10,116,194],"very":[11],"accurately,":[12],"it":[13],"is":[14,110,133,184],"well":[15,212],"known":[16],"that":[17,39],"the":[18,21,28,33,50,58,66,120,124,143,150,157,162,167,175,178,181,191,201],"attention":[19],"of":[20,32,52,169,180],"network":[22,132,159,183],"may":[23,69],"not":[24,47],"always":[25],"be":[26],"on":[27,166,230],"semantically":[29],"important":[30],"regions":[31],"scene.":[34],"It":[35],"has":[36,103],"been":[37],"observed":[38],"often":[41],"learn":[42],"background":[43,67,163,235],"textures,":[44],"which":[45,68,227],"are":[46,127],"relevant":[48],"to":[49,61,83,112,141,149,160,189,223],"object":[51,90,168],"interest.":[53,170],"In":[54],"turn":[55],"this":[56,85],"makes":[57],"susceptible":[60],"variations":[62],"and":[63,95,119,219,221,232],"changes":[64],"negatively":[70],"affect":[71],"their":[72],"performance.We":[73],"propose":[74],"a":[75,107,130,207],"new":[76],"three-step":[77],"training":[78,82,101,198,203],"procedure":[79,102,204],"called":[80],"split":[81,100],"reduce":[84],"bias":[86],"CNNs":[88,216],"for":[89,214],"recognition":[91],"using":[92,135,186],"Infrared":[93],"imagery":[94],"Color":[96],"(RGB)":[97],"data.":[98],"Our":[99,197],"three":[104],"steps.":[105],"First,":[106],"baseline":[108],"model":[109],"trained":[111,134,185],"recognize":[113],"without":[117],"background,":[118],"activations":[121,176],"produced":[122],"by":[123],"higher":[125,237],"layers":[126,173],"observed.":[128],"Next,":[129],"second":[131,158,182],"Mean":[136],"Square":[137],"Error":[138],"(MSE)":[139],"loss":[140,188],"produce":[142],"same":[144],"activations,":[145],"but":[146],"response":[148],"embedded":[152],"background.":[154,196],"This":[155],"forces":[156],"ignore":[161],"while":[164],"focusing":[165],"Finally,":[171],"with":[172,195,236],"producing":[174],"frozen,":[177],"rest":[179],"cross-entropy":[187],"method":[199],"outperforms":[200],"traditional":[202],"both":[206],"simple":[208],"CNN":[209],"architecture,":[210],"as":[211,213],"deep":[215],"like":[217],"VGG":[218],"DenseNet,":[220],"learns":[222],"mimic":[224],"human":[225],"vision":[226],"focuses":[228],"more":[229],"shape":[231],"structure":[233],"than":[234],"accuracy.":[238]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}